Statistics (Topics) for Machine Learning

Statistics is a subject and a branch of mathematics that is related to all the collection, analysis, interpretation, and visualization of empirical data, and there are two major areas of statistics are descriptive statistics and inferential statistics. If we talk about, descriptive statistics are used to describe the characteristics of sample and population data (what has happened). These properties are used by inferential statistics to test hypotheses, reach conclusions, and make predictions (what can you expect).

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Homogeneous Data Nodes

A Complete Guide to Decision Tree Formation and Interpretation in Machine Learning

Decision Tree is supervised machine learning algorithm which is used for both types of problems regression (that is predicting the continuous value for future example house price, hours the match can be played given overcast condition etc…) and classification (that is classifying different objects into respective categories or classes for example given the overcast conditions match will be played or not, given image belongs to cat or dog etc…).

Which industries will benefit most from Artificial Intelligence and Machine Learning in 2022

Artificial intelligence and machine learning are two of the hottest topics of discussion in recent times. We get to hear these terms very commonly due to their wide array of applications in different types of industries. Both these technologies are evolving at a great pace. Most importantly, a blend of the two makes it even more powerful. It is precisely the reason why the current trend lies in focusing on an appropriate combination of AI and ML to enhance its potential further. And this makes industries to gain more profits.

Precision-Recall vs ROC-AUC curve

What is the difference between Precision-Recall Curve vs ROC-AUC curve?

In Machine Learning, it is very important to have good understanding of different performance metrics. And it is even more important to know when to use which one to correctly explain the model performance. In classification problems more specific to binary classification, you can not conclude your model without plotting Precision-Recall curve and ROC-AUC curve. In this post, will learn what is the main difference between Precision-Recall curve and ROC-AUC curve and when to use which one.

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